Recent publications include:
2024
Artificial intelligence to advance Earth observation: a perspective, Devis Tuia, Konrad Schindler, Begüm Demir, Gustau Camps-Valls, Xiao Xiang Zhu, Mrinalini Kochupillai, Sašo Džeroski, Jan N. van Rijn, Holger H. Hoos, Fabio Del Frate, Mihai Datcu, Jorge-Arnulfo Quiané-Ruiz, Volker Markl, Bertrand Le Saux, Rochelle Schneider, arXiv:2305.08413, May 2023.
PhilEO Bench: Evaluating Geo-Spatial Foundation Models, Casper Fibaek, Luke Camilleri, Andreas Luyts, Nikolaos Dionelis, Bertrand Le Saux, arXiv:2401.04464, to appear IGARSS’2024, July 2024.
[ Project page / arxiv / pdf / GitHub / HugginFace / Dataset of downsteam tasks ]
Improving cross-site generalizability of vision-based solar forecasting models with physics-informed transfer learning Quentin Paletta; Yuhao Nie; Yves-Marie Saint-Drenan; Bertrand Le Saux, Energy Conversion and Management, April 2024.
[ article / doi / talk at ESA-ECMWF workshop ]
Land Cover Classification Refinement Through Image Segmentation, Jan Svoboda, Bertrand Le Saux, Peter Naylor, Josef Laštovička, Přemysl Štych, to appear at EARSeL Symposium 2024, Manchester, June 2024.
[ to appear ]
The PhilEO Geospatial Foundation Model Suite, Bertrand Le Saux, Casper Fibaek, Luke Camilleri, Andreas Luyts, Nikolaos Dionelis, Giacomo Donato Cascarano, Leonardo Bagaglini, and Giorgio Pasquali, EGU’24, Vienna, April 2024.
[ EGU Abstract / doi / Project page / arxiv / pdf / GitHub / HugginFace / Dataset of downsteam tasks ]
IceCloudNet: 3D reconstruction of cloud ice from Meteosat SEVIRI input, Kai Jeggle, Mikolaj Czerkawski, Federico Serva, Bertrand Le Saux, David Neubauer, and Ulrike Lohmann, EGU’24, Vienna, April 2024.
[ EGU Abstract / doi ]
Reconstructing 20th century burned area by combining global fire model input, satellite observations and machine learning, Seppe Lampe, Lukas Gudmundsson, Vincent Humphrey, Inne Vanderkelen, Bertrand Le Saux, and Wim Thiery, EGU’24, Vienna, April 2024.
[ EGU Abstract) / doi ]
Advancing Measurements and Observations in the Geosciences, Nick Everard, Bertrand Le Saux, Kirk Martinez, EGU’24, Vienna, April 2024.
[ EGU Union Symposium session ]
Squeezing adaptive deep learning methods with knowledge distillation for on-board cloud detection B. Grabowski, M. Ziaja, M. Kawulok, P. Bosowski, N. Longépé, B. Le Saux et J. Nalepa, Engineering Applications of Artificial Intelligence 132, p. 107835, January 2024.
2023
IceCloudNet: Cirrus and mixed-phase cloud prediction from SEVIRI input learned from sparse supervision Kai Jeggle, Mikolaj Czerkawski, Federico Serva, Bertrand Le Saux, David Neubauer, Ulrike Lohmann, NeurIPS / CCAI, New Orleans, LA, Dec. 2023
[ arxiv / CCAI abstract and paper @ NeurIPS’23 / NeurIPS video and slides ]
The curse of language biases in remote sensing VQA: the role of spatial attributes, language diversity, and the need for clear evaluation Christel Chappuis, Eliot Walt, Vincent Mendez, Sylvain Lobry, Bertrand Le Saux, Devis Tuia, to appear.
[ arxiv ]
ChangeMatch: A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR Imagery Murdaca, Gianluca; Ricciuti, Federico; Rucci, Alessio; Le Saux, Bertrand; Fumagalli, Alfio; Prati, Claudio, Remote Sensing 15 (24), December 2023.
[ Remote Sensing article / doi / arxiv to appear ]
Multi-task prompt-RSVQA to explicitly count objects on aerial images Christel Chappuis, Charlotte Sertic, Nicolas Santacroce, Javiera Castillo, Sylvain Lobry, Bertrand Le Saux, Devis Tuia, BMVC workshop on Machine Vision for Earth Observation, Aberdeen, UK, Nov. 2023.
[ BMVC workshop proceedings / pdf / arxiv ]
A Single-Step Multiclass SVM based on Quantum Annealing for Remote Sensing Data Classification Amer Delilbasic, Bertrand Le Saux, Morris Riedel, Kristel Michielsen, Gabriele Cavallaro, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), Vol. 16, November 2023 / QTML’23, Geneva, Swittzerland, Nov. 2023.
[ JSTARS article / doi / arxiv / pdf ]
Towards Strategies to Avoid Barren Plateaus, Sebastian Mair, Alessandro Sebastianelli, Andrea Ceschini, Samuel Vidal, Massimo Panella, Bertrand Le Saux, QTML’23, Geneva, Swittzerland, Nov. 2023.
[ abstract ]
Towards Quantum Diffusion Models, Francesca De Falco, Andrea Ceschini, Alessandro Sebastianelli, Massimo Panella, Bertrand Le Saux, QTML’23, Geneva, Swittzerland, Nov. 2023.
[ abstract ]
Approximately Equivariant Quantum Neural Network for p4m Group Symmetries in Images, Su-yeon Chang, M. Grossi, B. Le Saux, S. Vallecorsa, IEEE Quantum Week’23, Bellevue, WA, USA, Sep. 2023 / QTML’23, Geneva, Swittzerland, Nov. 2023.
[ abstract / IEEE QCE proceedings / arxiv ]
Quantum Machine Learning for Remote Sensing: Exploring potential and challenges Artur Miroszewski, Jakub Nalepa, Bertrand Le Saux, Jakub Mielczarek, Big Data from Space’23, Vienna, Austria, Nov. 2023
[ arxiv / BiDS proceedings / BiDS proc. #2 ]
Diffusion Models for Earth Observation Use-cases: from cloud removal to urban change detection Fulvio Sanguigni, Mikolaj Czerkawski, Lorenzo Papa, Irene Amerini, Bertrand Le Saux, Big Data from Space’23, Vienna, Austria, Nov. 2023
[ arxiv / BiDS proceedings / BiDS proc. #2 / code ]
Super-resolved rainfall prediction with physics-aware deep learning S Moran, B Demir, F Serva, B Le Saux, Big Data from Space’23, Vienna, Austria, Nov. 2023
[ arxiv / BiDS proceedings / BiDS proc. #2 ]
Deep-Learning-based Change Detection with Spaceborne Hyperspectral PRISMA data JF Amieva, A Austoni, MA Brovelli, L Ansalone, P Naylor, F Serva, B Le Saux, Big Data from Space’23, Vienna, Austria, Nov. 2023
[ arxiv / BiDS proceedings / BiDS proc. #2 ]
Weather4cast at NeurIPS 2022: Super-Resolution Rain Movie Prediction under Spatio-temporal Shifts A. Gruca, F. Serva, …., B. Le Saux, D. Kopp, S. Hochreiter, D. Kreil, Proceedings of Machine Learning Research, vol. 220, Proceedings of the NeurIPS 2022 Competitions Track, 292-313, 2023.
[ PMLR abstract / PMLR pdf ]
Detection of Forest Fires through Deep Unsupervised Learning Modeling of Sentinel-1 Time Series Thomas di Martino, Bertrand Le Saux, Régis Guinvarc’h, Laetitia Thirion-Lefevre, Elise Colin, ISPRS International Journal of Geo-Information, vol. 12, num. 8, August 2023.
[ IJGI version / doi ]
Detecting Clouds in Multispectral Satellite Images Using Quantum-Kernel Support Vector Machines Artur Miroszewski, Jakub Mielczarek, Grzegorz Czelusta, Filip Szczepanek, Bartosz Grabowski, Bertrand Le Saux, Jakub Nalepa, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), vol. 16, August 2023.
[ JSTARS article / doi / arxiv / pdf ]
Rapid Training of Quantum Recurrent Neural Networks, M. Siemaszko, A. Buraczewski, B. Le Saux and M. Stobińska, Quantum Machine Intelligence, vol. 2, num. 5, July 2023.
[ Open-access QMI html / Open-access QMI pdf / arxiv ]
2022 ECMWF-ESA workshop report: current status, progress and opportunities in machine learning for Earth system observation and prediction, Rochelle Schneider, Massimo Bonavita, Rossella Arcucci, Matthew Chantry, Marcin Chrust, Alan Geer, Bertrand Le Saux, and Claudia Vitolo, npj Climate and Atmospheric Science, vol. 6, July 2023.
[ npj article / 2022 ECMWF-ESA workshop at Reading ]
Knowledge distillation for memory-efficient on-board image classification of Mars imagery, Piotr Bosowski, Nicolas Longépé, Bertrand Le Saux, Jakub Nalepa, IGARSS 2023, July 2023, Pasadena, CA, USA.
Unbiased validation of hyperspectral unmixing algorithms, Lukasz Tulczyjew, Michal Kawulok, Nicolas Longépé, Bertrand Le Saux, Jakub Nalepa, IGARSS 2023, July 2023, Pasadena, CA, USA.
Cloud detection in multispectral satellite images using support vector machines with quantum kernels, Artur Miroszewski, Jakub Mielczarek, Filip Szczepanek, Grzegorz Czelusta, Bartosz Grabowski, Bertrand Le Saux, Jakub Nalepa, IGARSS 2023, July 2023, Pasadena, CA, USA.
Optimizing Kernel-Target Alignment for cloud detection in multispectral satellite images, Artur Miroszewski, Jakub Mielczarek, Filip Szczepanek, Grzegorz Czelusta, Bartosz Grabowski, Bertrand Le Saux, Jakub Nalepa, IGARSS 2023, July 2023, Pasadena, CA, USA.
Towards generation of synthetic hyperspectral image datasets with GAN, François De Vieilleville, Adrien Lagrange, Nicolas Dublé, and Bertrand Le Saux, EGU’2023, Vienna, Austria, April 2023.
[ EGU abstract / pdf / doi ]
Confidence estimation of DNN predictions for on-board applications, Nicolas Dublé, François De Vieilleville, Adrien Lagrange, and Bertrand Le Saux, EGU’2023, Vienna, Austria, April 2023.
[ EGU abstract / pdf / doi ]
Correlation between PQC Descriptors and Training Accuracy in Hybrid Quantum-Classical Model for Earth Observation Image Classification, Su-yeon Chang, B. Le Saux, S. Vallecorsa, M. Grossi, Quantum Information Processing (QIP 2023), Ghent, Belgium, February 2023.
[ QIP website ]
Multispectral Satellite Data Analysis Using Support Vector Machines With Quantum Kernels, Artur Miroszewski, F. Szczepanek, G. Czelusta, B. Grabowski, B. Le Saux, J. Nalepa and J. Mielczarek, Quantum Information Processing (QIP 2023), Ghent, Belgium, February 2023.
[ QIP website ]
2022
Report on the 2022 IEEE Geoscience and Remote Sensing Society Data Fusion Contest: Semisupervised Learning Hänsch, R.; Persello, C.; Vivone, G.; Castillo Navarro, J.; Boulch, A.; Lefèvre, S.; Le Saux, B., IEEE Geoscience and Remote Sensing Magazine, vol. 10, no. 4, pp. 270-273, Dec. 2022, doi: 10.1109/MGRS.2022.3219935.
[ IEEE GRSM version / doi / DFC 2022 benchmark / data ]
Rapid Training of Quantum Recurrent Neural Networks, M. Siemaszko, T. McDermott, A. Buraczewski, B. Le Saux and M. Stobińska, QTML’2022, Naples, Italy, Nov 2022.
[ arxiv ]
The Hyperview Challenge: Estimating Soil Parameters from Hyperspectral Images Jakub Nalepa, Bertrand Le Saux, Nicolas Longépé, Lukasz Tulczyjew, Michal Myller, Michal Kawulok, Krzysztof Smykala, Michal Gumiela, ICIP 2022, Bordeaux, France, October 2022.
[ editor version / doi / challenge ]
Learning Local Depth Regression from Defocus Blur by Soft-Assignment Encoding, R. Leroy, P. Trouvé, B. Le Saux, B. Buat, F. Champagnat, Optica Applied Optics, October 2022.
[ Appl. Opt. / pdf ]
Hybrid Quantum-Classical Networks for Reconstruction and Classification of Earth Observation Images, Su-yeon Chang, S. Vallecorsa, M. Grossi, B. Le Saux, 21st Int. Ws on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2022), Bari, Italy, October 2022.
[ ACAT abstract and material / slides ]
Multiclass SVM with Quantum Annealing, A. Delilbasic, G. Cavallaro, B. Le Saux, M. Riedel and K. Michielsen, Quantum Machine Learning workshop at ECML-PKDD 2022, Grenoble, France, Sep 2022.
Rapid Training of Quantum Recurrent Neural Network, M. Siemaszko, T. McDermott, A. Buraczewski, B. Le Saux and M. Stobińska, Quantum Machine Learning workshop at ECML-PKDD 2022, Grenoble, France, Sep 2022.
Language Transformers for Remote Sensing Visual Question Answering, C. Chappuis, V. Mendez, E. Walt, D. Tuia, S. Lobry, B. Le Saux, IGARSS 2022, July 2022.
[ IEEE pdf / IGARSS info ]
Weakly-supervised Continual Learning for Class-Incremental Segmentation, G. Lenczner, A. Chan-Hon-Tong, N. Luminari, B. Le Saux, IGARSS 2022, July 2022.
[ IEEE pdf / arxiv / IGARSS info / code ]
Quantum Convolutional Circuits for Earth Observation Image Classification, Su-yeon Chang, S. Vallecorsa, M. Grossi, B. Le Saux, IGARSS 2022, July 2022.
[ IEEE pdf / IGARSS info ]
ESA-ECMWF Report on recent progress and research directions in machine learning for Earth System observation and prediction, R. Schneider, M. Bonavita, A. Geer, R. Arcucci, P. Dueben, C. Vitolo, B. Le Saux, B. Demir & PP. Mathieu, npj climate and atmospheric science 5 (51), June 2022.
[ NPJ CAS version / doi / workshop website ml4esop.esa.int ]
A Multibranch Convolutional Neural Network for Hyperspectral Unmixing Lukasz Tulczyjew, Michal Kawulok, Nicolas Longépé, Bertrand Le Saux, Jakub Nalepa, IEEE Geoscience and Remote Sensing Letters (GRSL), June 2022.
[ editor version / arxiv ]
Graph Neural Networks Extract High-Resolution Cultivated Land Maps From Sentinel-2 Image Series Lukasz Tulczyjew, Michal Kawulok, Nicolas Longépé, Bertrand Le Saux, Jakub Nalepa, IEEE Geoscience and Remote Sensing Letters (GRSL), June 2022.
[ editor version / arxiv ]
Self-supervised learning – A way to minimize time and effort for precision agriculture? M. Marszalek, B. Le Saux, PP Mathieu, A. Nowakowski, D. Springer, ISPRS Congress 2022, June 2022.
Prompt-RSVQA: Prompting Visual Context to a Language Model for Remote Sensing Visual Question Answering C. Chappuis, V. Zermatten, S. Lobry, B. Le Saux, D. Tuia, CVPR 2022 / Earth Vision workshop, June 2022.
[ CVF page with abstract / CVPR/EV22 pdf / arxiv to appear ]
2022 IEEE GRSS Data Fusion Contest: Semi-Supervised Learning Hänsch, R.; Persello, C.; Vivone, G.; Castillo Navarro, J.; Boulch, A.; Lefèvre, S.; Le Saux, B., IEEE Geoscience and Remote Sensing Magazine (GRSM), March 2022.
[ editor version / DFC 2022 benchmark / data ]
DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote Sensing G. Lenczner, A. Chan-Hon-Tong, B. Le Saux, N. Luminari, G. Le Besnerais, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), March 2022.
[ editor version / arxiv / DIAL code ]
Deep Learning for Archaeological Object Detection on LiDAR: New Evaluation Measures and Insights Marco Fiorucci, Wouter B. Verschoof-van der Vaart, Paolo Soleni, Bertrand Le Saux and Arianna Traviglia, Remote Sensing, March 2022.